2018

If you continue to browse this website, you accept third-party cookies used to offer you videos, social sharing buttons, contents from social platforms..

OK, accept all

Personnalize

Please check an answer for every question.

Deny all

Accept all

We use cookies to personalise content and to analyse our traffic. We also share information about your use on our site with our analytics partners.
They may combine it with other information that you provided them or that they collected from your use of their services.

Unlocking a mature asset’s full value requires evaluating reserves, that is, predicting its production. To do that, the oil industry uses reservoir models that engineers first try to calibrate to production history. This crucial “matching” step is time-consuming and often ineffective. Our post-matching models don’t always align with past observations and yield predictions of varying reliability. Using the Ensemble History Matching (EnHM) method produces better field-history matching and more reliable predictions, while reducing the research time required.

EnHM, a Commercial-Scale Process Unique in the Oil and Gas Industry

Standard matching methods are lengthy, painstaking and costly: it can take years to match a reservoir model to a mature field with many wells. Yet they still fall short, because they don’t take into account many uncertainties, in effect introducing an element of subjectivity.

That’s why we turned to the ensemble method, first used in 2003 by the International Research Institute of Stavanger (IRIS) to match reservoir models. Originally for weather forecasting models, this method isn’t sensitive to the number of uncertain parameters. It’s based on an innovative way to mathematically structure the problem.

After 10 years of research initiated in our Aberdeen research center, which specializes in geosciences, followed by development work at the Pau engineering and research center, our EnHM approach is unlike anything else in the industry. Powered by our Pangea supercomputer and a geomodeling approach integrated into the Sismage-CIG platform, it offers a number of advantages, including:

Integrating and optimizing a huge number of variables (hundreds of millions in each model) paired with degrees of uncertainty.

Proposing solutions that are geologically and geophysically consistent.

Fast-tracking implementation and execution to produce a set of models (not one) based on actual data.

This mature method can automatically perform round-robin matching of several dozen models, while taking into account major uncertainties concerning the reservoir's actual geometry or rock and fluid parameters, measured very locally. The result is better-quality matching of production data history and, ultimately, much more robust prediction profiles.

TwoLarge-Scale Pilots

Commercially scaled up in 2016, the EnHM method has already been applied twice, on giant fields in the Middle East:

In the first case, on an offshore field numerous complexities related to diagenesis and lithologic variations. A seismic survey followed by a stochastic inversion of the data had already been performed in 2015 to better understand the lithologies and the distribution of petrophysical parameters. Only the EnHM method was able to analyze the many uncertainties concerning both the petrophysical properties and geology. Thanks to EnHM, the team of two reservoir engineers delivered roughly 100 models, confirmed after matching to be geologically consistent.

The second cas posed an even more complex matching problem: a huge number of wells; many heterogeneities linked to the fracture and dissolution networks; an uncertain tilted oil-water contact; and lateral variations in fluid properties caused by a complex load history during oil and gas migration. And yet the EnHM method matched the field’s history to a preliminary model. It took only two months to match the field history and a model containing 20 million cells, based on 200 wells in production for over 20 years.

The EnHM method offers a clear competitive advantage and is expected to be used in future studies of mature fields of all sizes and complexity. By drastically shortening turnaround (and costs) for recalibrating reservoir models, it will help speed up the redevelopment of mature resources of strategic importance to Total.